Exploiting explicit and implicit feedback for personalized ranking
type: Item
instance of: scholarly article
title: Exploiting explicit and implicit feedback for personalized ranking
zbMATH Open document ID: 1400.68213
DOI: 10.1155/2016/2535329
author: Yanyan Li
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset. Until now, nobody has studied personalized ranking algorithm by exploiting both explicit and implicit feedback. In order to overcome the defects of prior researches, a new personalized ranking algorithm (MERR\(\_\)SVD++) based on the newest xCLiMF model and SVD++ algorithm was proposed, which exploited both explicit and implicit feedback simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR). Experimental results on practical datasets showed that our proposed algorithm outperformed existing personalized ranking algorithms over different evaluation metrics and that the running time of MERR\(\_\)SVD++ showed a linear correlation with the number of rating. Because of its high precision and the good expansibility, MERR\(\_\)SVD++ is suitable for processing big data and has wide application prospect in the field of internet information recommendation.
Mathematics Subject Classification ID: 68T35
Mathematics Subject Classification ID: 68T20
zbMATH DE Number: 6952925
Wikidata QID: Q59131156
describes a project that uses: EigenRank
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/2535329
OpenAlex ID: W2295601453
cites work: Learning to Rank for Information Retrieval
description: scientific article; zbMATH DE number 6952925